The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instrumen...The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.展开更多
高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集,目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响,提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial...高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集,目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响,提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model),VISTFM采用模糊C聚类算法,对存量时序VI数据按土地利用类型划分为若干子类,从高低分辨率影像中随土地覆被类的变化规律提取子类,结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法,测试表明,VISTFM能够较好的捕获VI的中间变化过程,与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比,利用VISTFM获得的植被指数数据集具有更高的时空分辨率。展开更多
There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces. A widely used parameter for such density, i.e., leaf area index （LAI）, was measured in situ in...There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces. A widely used parameter for such density, i.e., leaf area index （LAI）, was measured in situ in Nanjing, China and then correlated with two vegetation indices （VI） derived from multiple radiometric correction levels of a SPOT5 imagery. The VIs were a normal- ized difference vegetation index （NDVI） and a ratio vegetation index （RVI）, while the four radiometric correction levels were i） post atmospheric correction reflectance （PAC）, ii） top of atmosphere reflectance （TOA）, iii） satellite radiance （SR） and iv） digital number （DN）. A total of 157 LAI-VI relationship models were established. The results showed that LA! is positively correlated with VI （r varies from 0.303 to 0.927, p 〈 0.001）. The R： values of＂pure＂ vegetation were generally higher than those of mixed vegetation. The average R2 values of about 40 models based on DN data （0.688） were higher than that of the routinely used PAC （0.648）. Independent variables of the optimal models for different vegetation quadrats included two vegetation indices at three radiometric correction lev- els, indicating the potential of vegetation indices at multiple radiometric correction levels in LAI inversion. The study demonstrates that taking heterogeneities of vegetation structures and uncertainties of radiometric corrections into account may help full mining of valuable information from remote sensing images, thus improving accuracies of LAI estimation.展开更多
Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find th...Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles(UAVs).The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments.Individual spectral bands and different combinations of the plots,growth stages,and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling.A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution.The results showed that utilizing Green,Blue,and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates.Additionally,the combination of plots and growth stages outperformed all the candidates of random effects.The proposed model outperformed the Multiple Linear Regression(MLR),Generalized Linear Model(GLM),and Generalized Additive Model(GAM)for wet and dry sugarcane biomass,with coefficients of determination(R2)of 0.93 and 0.97,and Root Mean Square Error(RMSE)of 12.78 and 2.57 t/ha,respectively.This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices(VIs)in mature growth stages.展开更多
Due to infrequent rainfall, high temperatures, and degraded land, the Sahel region often suffers from droughts. The Sahel region is considered as one of the world’s driest and extreme environmental conditions. In ord...Due to infrequent rainfall, high temperatures, and degraded land, the Sahel region often suffers from droughts. The Sahel region is considered as one of the world’s driest and extreme environmental conditions. In order to assess spatiotemporal vulnerability of potential drought impacts, we used remote sensing and ground station data to evaluate drought conditions in the Sahel region from 1985 to 2015. The standard precipitation index(SPI), standard precipitation evapotranspiration index(SPEI), vegetation condition index(VCI) anomaly, along with socioeconomic indicators were performed. In addition, Pearson correlation coefficient(PCC) was computed between drought indices and three main crops(sorghum, millet, and maize) in the region to estimate the effects. The analysis showed that temperature increased by 0.78°C from 1985 to 2015, which had a significant impact on crop yield for sorghum, maize, and millet with a statistical significance value of P > 0.05. In the decade spanning 1994 to 2005 alone, the temperature increased by 0.57°C, which resulted in extreme drought in Algeria, Sudan, Chad, Nigeria, and Mauritania. For the effect of drought on crop production, high significance was noted on the SPI and SPEI-3 timescale: sorghum with SPI-3(r = 0.71) and SPEI-3(r = 0.65), millet with SPI-3(r = 0.61) and SPEI-3(r = 0.72), and maize with SPI-3(r =0.81) and SPEI-3(r = 0.65) during the study period. In the growing season, VCI anomaly had strong correlations with sorghum and millet(r = 0.67 and 0.75, respectively). A significant agreement was also noticed between the combined drought index(CDI) and vulnerability index(VI) in Burkina Faso(r =-0.676;P < 0.00), Mali(r =-0.768;P < 0.00), Mauritania(r = 0.843;P < 0.001), Niger(r =-0.625;P < 0.001), and Nigeria(r =-0.75;P < 0.005). The results show that the above indices are effective in assessing agricultural drought and its impact on crop production in the Sahel, and in identifying areas most affected by drought.展开更多
基金Supported by the National Key Research and Development Program of China(2018YFC1506500)。
文摘The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.
文摘高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集,目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响,提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model),VISTFM采用模糊C聚类算法,对存量时序VI数据按土地利用类型划分为若干子类,从高低分辨率影像中随土地覆被类的变化规律提取子类,结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法,测试表明,VISTFM能够较好的捕获VI的中间变化过程,与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比,利用VISTFM获得的植被指数数据集具有更高的时空分辨率。
基金funded by the National Natural Science Foundation of China(Grant No.41071281)
文摘There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces. A widely used parameter for such density, i.e., leaf area index （LAI）, was measured in situ in Nanjing, China and then correlated with two vegetation indices （VI） derived from multiple radiometric correction levels of a SPOT5 imagery. The VIs were a normal- ized difference vegetation index （NDVI） and a ratio vegetation index （RVI）, while the four radiometric correction levels were i） post atmospheric correction reflectance （PAC）, ii） top of atmosphere reflectance （TOA）, iii） satellite radiance （SR） and iv） digital number （DN）. A total of 157 LAI-VI relationship models were established. The results showed that LA! is positively correlated with VI （r varies from 0.303 to 0.927, p 〈 0.001）. The R： values of＂pure＂ vegetation were generally higher than those of mixed vegetation. The average R2 values of about 40 models based on DN data （0.688） were higher than that of the routinely used PAC （0.648）. Independent variables of the optimal models for different vegetation quadrats included two vegetation indices at three radiometric correction lev- els, indicating the potential of vegetation indices at multiple radiometric correction levels in LAI inversion. The study demonstrates that taking heterogeneities of vegetation structures and uncertainties of radiometric corrections into account may help full mining of valuable information from remote sensing images, thus improving accuracies of LAI estimation.
文摘Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles(UAVs).The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments.Individual spectral bands and different combinations of the plots,growth stages,and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling.A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution.The results showed that utilizing Green,Blue,and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates.Additionally,the combination of plots and growth stages outperformed all the candidates of random effects.The proposed model outperformed the Multiple Linear Regression(MLR),Generalized Linear Model(GLM),and Generalized Additive Model(GAM)for wet and dry sugarcane biomass,with coefficients of determination(R2)of 0.93 and 0.97,and Root Mean Square Error(RMSE)of 12.78 and 2.57 t/ha,respectively.This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices(VIs)in mature growth stages.
基金Supported by the National Key Research and Development Program of China(2019YFC1510203 and 2018YFC1506502)Fundamental Research Funds for Central Non-profit Scientific Institution(1610132020014)Open Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201910)。
文摘Due to infrequent rainfall, high temperatures, and degraded land, the Sahel region often suffers from droughts. The Sahel region is considered as one of the world’s driest and extreme environmental conditions. In order to assess spatiotemporal vulnerability of potential drought impacts, we used remote sensing and ground station data to evaluate drought conditions in the Sahel region from 1985 to 2015. The standard precipitation index(SPI), standard precipitation evapotranspiration index(SPEI), vegetation condition index(VCI) anomaly, along with socioeconomic indicators were performed. In addition, Pearson correlation coefficient(PCC) was computed between drought indices and three main crops(sorghum, millet, and maize) in the region to estimate the effects. The analysis showed that temperature increased by 0.78°C from 1985 to 2015, which had a significant impact on crop yield for sorghum, maize, and millet with a statistical significance value of P > 0.05. In the decade spanning 1994 to 2005 alone, the temperature increased by 0.57°C, which resulted in extreme drought in Algeria, Sudan, Chad, Nigeria, and Mauritania. For the effect of drought on crop production, high significance was noted on the SPI and SPEI-3 timescale: sorghum with SPI-3(r = 0.71) and SPEI-3(r = 0.65), millet with SPI-3(r = 0.61) and SPEI-3(r = 0.72), and maize with SPI-3(r =0.81) and SPEI-3(r = 0.65) during the study period. In the growing season, VCI anomaly had strong correlations with sorghum and millet(r = 0.67 and 0.75, respectively). A significant agreement was also noticed between the combined drought index(CDI) and vulnerability index(VI) in Burkina Faso(r =-0.676;P < 0.00), Mali(r =-0.768;P < 0.00), Mauritania(r = 0.843;P < 0.001), Niger(r =-0.625;P < 0.001), and Nigeria(r =-0.75;P < 0.005). The results show that the above indices are effective in assessing agricultural drought and its impact on crop production in the Sahel, and in identifying areas most affected by drought.
